Larissa Höfling

openretina provides a standardized framework for training and evaluating retinal models across datasets and species, revealing unexplained variance in current models.

The paper identifies a retinal ganglion cell type that detects visual context changes by responding selectively to chromatic contrast transitions.

This study improves neural system identification by incorporating efficient coding principles, showing better performance and biologically plausible filters in retinal neuron response prediction.

This paper develops a domain adaptation framework to remove inter-experimental variability in systems neuroscience data while preserving biological signals, showing improved performance over existing methods.

The paper identifies linear filters of retinal ganglion cells using a smooth electrical stimulus and an LNP model, showing diverse preferred stimuli in healthy and blind mouse retinas.